
"""simple docstring"""


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


SCREAMING_SNAKE_CASE			  =  R"""
    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.

    Args:
        title_sep (`str`, *optional*, defaults to  `\" / \"`):
            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].
        doc_sep (`str`, *optional*, defaults to  `\" // \"`):
            Separator inserted between the text of the retrieved document and the original input when calling
            [`RagRetriever`].
        n_docs (`int`, *optional*, defaults to 5):
            Number of documents to retrieve.
        max_combined_length (`int`, *optional*, defaults to 300):
            Max length of contextualized input returned by [`~RagRetriever.__call__`].
        retrieval_vector_size (`int`, *optional*, defaults to 768):
            Dimensionality of the document embeddings indexed by [`RagRetriever`].
        retrieval_batch_size (`int`, *optional*, defaults to 8):
            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated
            [`RagRetriever`].
        dataset (`str`, *optional*, defaults to `\"wiki_dpr\"`):
            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids
            using `datasets.list_datasets()`).
        dataset_split (`str`, *optional*, defaults to `\"train\"`)
            Which split of the `dataset` to load.
        index_name (`str`, *optional*, defaults to `\"compressed\"`)
            The index name of the index associated with the `dataset`. One can choose between `\"legacy\"`, `\"exact\"` and
            `\"compressed\"`.
        index_path (`str`, *optional*)
            The path to the serialized faiss index on disk.
        passages_path (`str`, *optional*):
            A path to text passages compatible with the faiss index. Required if using
            [`~models.rag.retrieval_rag.LegacyIndex`]
        use_dummy_dataset (`bool`, *optional*, defaults to `False`)
            Whether to load a \"dummy\" variant of the dataset specified by `dataset`.
        label_smoothing (`float`, *optional*, defaults to 0.0):
            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing
            in the loss calculation. If set to 0, no label smoothing is performed.
        do_marginalize (`bool`, *optional*, defaults to `False`):
            If `True`, the logits are marginalized over all documents by making use of
            `torch.nn.functional.log_softmax`.
        reduce_loss (`bool`, *optional*, defaults to `False`):
            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.
        do_deduplication (`bool`, *optional*, defaults to `True`):
            Whether or not to deduplicate the generations from different context documents for a given input. Has to be
            set to `False` if used while training with distributed backend.
        exclude_bos_score (`bool`, *optional*, defaults to `False`):
            Whether or not to disregard the BOS token when computing the loss.
        output_retrieved(`bool`, *optional*, defaults to `False`):
            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and
            `context_attention_mask` are returned. See returned tensors for more detail.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
        forced_eos_token_id (`int`, *optional*):
            The id of the token to force as the last generated token when `max_length` is reached. Usually set to
            `eos_token_id`.
"""



@add_start_docstrings(_lowerCAmelCase						)
class 	__a	(   _lowerCAmelCase						):
       UpperCamelCase_		:       Dict				=      '''rag'''
       UpperCamelCase_		:       int				=      True






       def __init__(	self			:				Tuple							,   UpperCAmelCase_			:				Any=None							,   UpperCAmelCase_			:				Tuple=True							,   UpperCAmelCase_			:				str=None							,   UpperCAmelCase_			:				Tuple=None							,   UpperCAmelCase_			:				Union[str, Any]=None							,   UpperCAmelCase_			:				Any=None							,   UpperCAmelCase_			:				Union[str, Any]=None							,   UpperCAmelCase_			:				str=" / "							,   UpperCAmelCase_			:				Union[str, Any]=" // "							,   UpperCAmelCase_			:				str=5							,   UpperCAmelCase_			:				str=300							,   UpperCAmelCase_			:				List[Any]=768							,   UpperCAmelCase_			:				Optional[Any]=8							,   UpperCAmelCase_			:				List[str]="wiki_dpr"							,   UpperCAmelCase_			:				Union[str, Any]="train"							,   UpperCAmelCase_			:				Optional[Any]="compressed"							,   UpperCAmelCase_			:				Optional[int]=None							,   UpperCAmelCase_			:				int=None							,   UpperCAmelCase_			:				Optional[int]=False							,   UpperCAmelCase_			:				Dict=False							,   UpperCAmelCase_			:				Tuple=0.0							,   UpperCAmelCase_			:				Dict=True							,   UpperCAmelCase_			:				Optional[int]=False							,   UpperCAmelCase_			:				int=False							,   UpperCAmelCase_			:				Any=False							,   UpperCAmelCase_			:				Union[str, Any]=True							,   UpperCAmelCase_			:				Tuple=None							,   **UpperCAmelCase_			:				Union[str, Any]							,   )->	List[Any]:







              """simple docstring"""
              super().__init__(
                  bos_token_id=UpperCAmelCase_							,   pad_token_id=UpperCAmelCase_							,   eos_token_id=UpperCAmelCase_							,   decoder_start_token_id=UpperCAmelCase_							,   forced_eos_token_id=UpperCAmelCase_							,   is_encoder_decoder=UpperCAmelCase_							,   prefix=UpperCAmelCase_							,   vocab_size=UpperCAmelCase_							,   **UpperCAmelCase_							,   )
              assert (
                  "question_encoder" in kwargs and "generator" in kwargs
              ), "Config has to be initialized with question_encoder and generator config"
              UpperCamelCase       			=		kwargs.pop("question_encoder"      )
              UpperCamelCase       			=		question_encoder_config.pop("model_type"      )
              UpperCamelCase       			=		kwargs.pop("generator"      )
              UpperCamelCase       			=		decoder_config.pop("model_type"      )

              from ..auto.configuration_auto import AutoConfig

              UpperCamelCase       			=		AutoConfig.for_model(UpperCAmelCase_							,   **UpperCAmelCase_      )
              UpperCamelCase       			=		AutoConfig.for_model(UpperCAmelCase_							,   **UpperCAmelCase_      )

              UpperCamelCase       			=		reduce_loss
              UpperCamelCase       			=		label_smoothing
              UpperCamelCase       			=		exclude_bos_score
              UpperCamelCase       			=		do_marginalize

              UpperCamelCase       			=		title_sep
              UpperCamelCase       			=		doc_sep
              UpperCamelCase       			=		n_docs
              UpperCamelCase       			=		max_combined_length

              UpperCamelCase       			=		dataset
              UpperCamelCase       			=		dataset_split
              UpperCamelCase       			=		index_name

              UpperCamelCase       			=		retrieval_vector_size
              UpperCamelCase       			=		retrieval_batch_size
              UpperCamelCase       			=		passages_path
              UpperCamelCase       			=		index_path
              UpperCamelCase       			=		use_dummy_dataset

              UpperCamelCase       			=		output_retrieved

              UpperCamelCase       			=		do_deduplication

              UpperCamelCase       			=		use_cache

              if self.forced_eos_token_id is None:
                     UpperCamelCase       			=		getattr(self.generator							,   "forced_eos_token_id"							,   UpperCAmelCase_      )






       @classmethod
       def 						_SCREAMING_SNAKE_CASE							(	cls			:				Union[str, Any]							,   UpperCAmelCase_			:				PretrainedConfig							,   UpperCAmelCase_			:				PretrainedConfig							,   **UpperCAmelCase_			:				Optional[Any]      )->	PretrainedConfig:







              """simple docstring"""
              return cls(question_encoder=question_encoder_config.to_dict()							,   generator=generator_config.to_dict()							,   **UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				str      )->	List[Any]:







              """simple docstring"""
              UpperCamelCase       			=		copy.deepcopy(self.__dict__      )
              UpperCamelCase       			=		self.question_encoder.to_dict()
              UpperCamelCase       			=		self.generator.to_dict()
              UpperCamelCase       			=		self.__class__.model_type
              return output







"""simple docstring"""


import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser


SCREAMING_SNAKE_CASE			  =  logging.getLogger(__name__)
torch.set_grad_enabled(False)
SCREAMING_SNAKE_CASE			  =  """cuda""" if torch.cuda.is_available() else """cpu"""



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_=1_00		,   UpperCAmelCase_=" "					)->				List[str]:







       """simple docstring"""
       UpperCamelCase       			=		text.split(UpperCAmelCase_					)
       return [character.join(text[i : i + n]					).strip() for i in range(0		,   len(UpperCAmelCase_					)		,   UpperCAmelCase_					)]



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				dict:







       """simple docstring"""
       UpperCamelCase			,							UpperCamelCase       			=		[], []
       for title, text in zip(documents["title"]		,   documents["text"]					):
              if text is not None:
                     for passage in split_text(UpperCAmelCase_					):
                            titles.append(title if title is not None else ""					)
                            texts.append(UpperCAmelCase_					)
       return {"title": titles, "text": texts}



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				dict:







       """simple docstring"""
       UpperCamelCase       			=		ctx_tokenizer(
           documents["title"]		,   documents["text"]		,   truncation=UpperCAmelCase_		,   padding="longest"		,   return_tensors="pt"					)["input_ids"]
       UpperCamelCase       			=		ctx_encoder(input_ids.to(device=UpperCAmelCase_					)		,   return_dict=UpperCAmelCase_					).pooler_output
       return {"embeddings": embeddings.detach().cpu().numpy()}



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   )->				List[str]:







       """simple docstring"""
       ######################################
       logger.info("Step 1 - Create the dataset"					)
       ######################################

       # The dataset needed for RAG must have three columns:
       # - title (string): title of the document
       # - text (string): text of a passage of the document
       # - embeddings (array of dimension d): DPR representation of the passage
       # Let's say you have documents in tab-separated csv files with columns "title" and "text"
       assert os.path.isfile(rag_example_args.csv_path					), "Please provide a valid path to a csv file"

       # You can load a Dataset object this way
       UpperCamelCase       			=		load_dataset(
           "csv"		,   data_files=[rag_example_args.csv_path]		,   split="train"		,   delimiter="\t"		,   column_names=["title", "text"]					)

       # More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

       # Then split the documents into passages of 100 words
       UpperCamelCase       			=		dataset.map(UpperCAmelCase_		,   batched=UpperCAmelCase_		,   num_proc=processing_args.num_proc					)

       # And compute the embeddings
       UpperCamelCase       			=		DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					).to(device=UpperCAmelCase_					)
       UpperCamelCase       			=		DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					)
       UpperCamelCase       			=		Features(
           {"text": Value("string"					), "title": Value("string"					), "embeddings": Sequence(Value("float32"					)					)}					)  # optional, save as float32 instead of float64 to save space
       UpperCamelCase       			=		dataset.map(
           partial(UpperCAmelCase_		,   ctx_encoder=UpperCAmelCase_		,   ctx_tokenizer=UpperCAmelCase_					)		,   batched=UpperCAmelCase_		,   batch_size=processing_args.batch_size		,   features=UpperCAmelCase_		,   )

       # And finally save your dataset
       UpperCamelCase       			=		os.path.join(rag_example_args.output_dir		,   "my_knowledge_dataset"					)
       dataset.save_to_disk(UpperCAmelCase_					)
       # from datasets import load_from_disk
       # dataset = load_from_disk(passages_path)  # to reload the dataset

       ######################################
       logger.info("Step 2 - Index the dataset"					)
       ######################################

       # Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
       UpperCamelCase       			=		faiss.IndexHNSWFlat(index_hnsw_args.d		,   index_hnsw_args.m		,   faiss.METRIC_INNER_PRODUCT					)
       dataset.add_faiss_index("embeddings"		,   custom_index=UpperCAmelCase_					)

       # And save the index
       UpperCamelCase       			=		os.path.join(rag_example_args.output_dir		,   "my_knowledge_dataset_hnsw_index.faiss"					)
       dataset.get_index("embeddings"					).save(UpperCAmelCase_					)
       # dataset.load_faiss_index("embeddings", index_path)  # to reload the index



@dataclass
class 	__a	:
       UpperCamelCase_		:       str				=      field(
           default=str(Path(_lowerCAmelCase						).parent / '''test_run''' / '''dummy-kb''' / '''my_knowledge_dataset.csv'''						)     ,  metadata={'''help''': '''Path to a tab-separated csv file with columns \'title\' and \'text\''''}     ,  )
       UpperCamelCase_		:       Optional[str]				=      field(
           default=_lowerCAmelCase     ,  metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''}     ,  )
       UpperCamelCase_		:       str				=      field(
           default='''facebook/rag-sequence-nq'''     ,  metadata={'''help''': '''The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''''}     ,  )
       UpperCamelCase_		:       str				=      field(
           default='''facebook/dpr-ctx_encoder-multiset-base'''     ,  metadata={
               '''help''': (
                   '''The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'''
                   ''' \'facebook/dpr-ctx_encoder-multiset-base\''''
               )
           }     ,  )
       UpperCamelCase_		:       Optional[str]				=      field(
           default=str(Path(_lowerCAmelCase						).parent / '''test_run''' / '''dummy-kb'''						)     ,  metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''}     ,  )





@dataclass
class 	__a	:
       UpperCamelCase_		:       Optional[int]				=      field(
           default=_lowerCAmelCase     ,  metadata={
               '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
           }     ,  )
       UpperCamelCase_		:       int				=      field(
           default=16     ,  metadata={
               '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
           }     ,  )





@dataclass
class 	__a	:
       UpperCamelCase_		:       int				=      field(
           default=768     ,  metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}     ,  )
       UpperCamelCase_		:       int				=      field(
           default=128     ,  metadata={
               '''help''': (
                   '''The number of bi-directional links created for every new element during the HNSW index construction.'''
               )
           }     ,  )


if __name__ == "__main__":
      logging.basicConfig(level=logging.WARNING)
      logger.setLevel(logging.INFO)

      SCREAMING_SNAKE_CASE			  =  HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
      SCREAMING_SNAKE_CASE  ,   SCREAMING_SNAKE_CASE  ,   SCREAMING_SNAKE_CASE			  =  parser.parse_args_into_dataclasses()
      with TemporaryDirectory() as tmp_dir:
            SCREAMING_SNAKE_CASE			  =  rag_example_args.output_dir or tmp_dir
            main(rag_example_args, processing_args, index_hnsw_args)





